<!doctype html>
<html>
<head>
<meta charset='UTF-8'><meta name='viewport' content='width=device-width initial-scale=1'>

<link href='https://fonts.loli.net/css?family=PT+Serif:400,400italic,700,700italic&subset=latin,cyrillic-ext,cyrillic,latin-ext' rel='stylesheet' type='text/css' /><style type='text/css'>html {overflow-x: initial !important;}:root { --bg-color:#ffffff; --text-color:#333333; --select-text-bg-color:#B5D6FC; --select-text-font-color:auto; --monospace:"Lucida Console",Consolas,"Courier",monospace; --title-bar-height:20px; }
.mac-os-11 { --title-bar-height:28px; }
html { font-size: 14px; background-color: var(--bg-color); color: var(--text-color); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -webkit-font-smoothing: antialiased; }
body { margin: 0px; padding: 0px; height: auto; inset: 0px; font-size: 1rem; line-height: 1.42857; overflow-x: hidden; background: inherit; tab-size: 4; }
iframe { margin: auto; }
a.url { word-break: break-all; }
a:active, a:hover { outline: 0px; }
.in-text-selection, ::selection { text-shadow: none; background: var(--select-text-bg-color); color: var(--select-text-font-color); }
#write { margin: 0px auto; height: auto; width: inherit; word-break: normal; overflow-wrap: break-word; position: relative; white-space: normal; overflow-x: visible; padding-top: 36px; }
#write.first-line-indent p { text-indent: 2em; }
#write.first-line-indent li p, #write.first-line-indent p * { text-indent: 0px; }
#write.first-line-indent li { margin-left: 2em; }
.for-image #write { padding-left: 8px; padding-right: 8px; }
body.typora-export { padding-left: 30px; padding-right: 30px; }
.typora-export .footnote-line, .typora-export li, .typora-export p { white-space: pre-wrap; }
.typora-export .task-list-item input { pointer-events: none; }
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  .CodeMirror-gutters { display: none !important; }
}
#write li > figure:last-child { margin-bottom: 0.5rem; }
#write ol, #write ul { position: relative; }
img { max-width: 100%; vertical-align: middle; image-orientation: from-image; }
button, input, select, textarea { color: inherit; font: inherit; }
input[type="checkbox"], input[type="radio"] { line-height: normal; padding: 0px; }
*, ::after, ::before { box-sizing: border-box; }
#write h1, #write h2, #write h3, #write h4, #write h5, #write h6, #write p, #write pre { width: inherit; }
#write h1, #write h2, #write h3, #write h4, #write h5, #write h6, #write p { position: relative; }
p { line-height: inherit; }
h1, h2, h3, h4, h5, h6 { break-after: avoid-page; break-inside: avoid; orphans: 4; }
p { orphans: 4; }
h1 { font-size: 2rem; }
h2 { font-size: 1.8rem; }
h3 { font-size: 1.6rem; }
h4 { font-size: 1.4rem; }
h5 { font-size: 1.2rem; }
h6 { font-size: 1rem; }
.md-math-block, .md-rawblock, h1, h2, h3, h4, h5, h6, p { margin-top: 1rem; margin-bottom: 1rem; }
.hidden { display: none; }
.md-blockmeta { color: rgb(204, 204, 204); font-weight: 700; font-style: italic; }
a { cursor: pointer; }
sup.md-footnote { padding: 2px 4px; background-color: rgba(238, 238, 238, 0.7); color: rgb(85, 85, 85); border-radius: 4px; cursor: pointer; }
sup.md-footnote a, sup.md-footnote a:hover { color: inherit; text-transform: inherit; text-decoration: inherit; }
#write input[type="checkbox"] { cursor: pointer; width: inherit; height: inherit; }
figure { overflow-x: auto; margin: 1.2em 0px; max-width: calc(100% + 16px); padding: 0px; }
figure > table { margin: 0px; }
tr { break-inside: avoid; break-after: auto; }
thead { display: table-header-group; }
table { border-collapse: collapse; border-spacing: 0px; width: 100%; overflow: auto; break-inside: auto; text-align: left; }
table.md-table td { min-width: 32px; }
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.md-fences.mock-cm.md-fences-with-lineno { padding-left: 8px; }
.CodeMirror-line, twitterwidget { break-inside: avoid; }
.footnotes { opacity: 0.8; font-size: 0.9rem; margin-top: 1em; margin-bottom: 1em; }
.footnotes + .footnotes { margin-top: 0px; }
.md-reset { margin: 0px; padding: 0px; border: 0px; outline: 0px; vertical-align: top; background: 0px 0px; text-decoration: none; text-shadow: none; float: none; position: static; width: auto; height: auto; white-space: nowrap; cursor: inherit; -webkit-tap-highlight-color: transparent; line-height: normal; font-weight: 400; text-align: left; box-sizing: content-box; direction: ltr; }
li div { padding-top: 0px; }
blockquote { margin: 1rem 0px; }
li .mathjax-block, li p { margin: 0.5rem 0px; }
li blockquote { margin: 1rem 0px; }
li { margin: 0px; position: relative; }
blockquote > :last-child { margin-bottom: 0px; }
blockquote > :first-child, li > :first-child { margin-top: 0px; }
.footnotes-area { color: rgb(136, 136, 136); margin-top: 0.714rem; padding-bottom: 0.143rem; white-space: normal; }
#write .footnote-line { white-space: pre-wrap; }
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.footnote-line { margin-top: 0.714em; font-size: 0.7em; }
a img, img a { cursor: pointer; }
pre.md-meta-block { font-size: 0.8rem; min-height: 0.8rem; white-space: pre-wrap; background: rgb(204, 204, 204); display: block; overflow-x: hidden; }
p > .md-image:only-child:not(.md-img-error) img, p > img:only-child { display: block; margin: auto; }
#write.first-line-indent p > .md-image:only-child:not(.md-img-error) img { left: -2em; position: relative; }
p > .md-image:only-child { display: inline-block; width: 100%; }
#write .MathJax_Display { margin: 0.8em 0px 0px; }
.md-math-block { width: 100%; }
.md-math-block:not(:empty)::after { display: none; }
.MathJax_ref { fill: currentcolor; }
[contenteditable="true"]:active, [contenteditable="true"]:focus, [contenteditable="false"]:active, [contenteditable="false"]:focus { outline: 0px; box-shadow: none; }
.md-task-list-item { position: relative; list-style-type: none; }
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.math { font-size: 1rem; }
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.md-toc-h3 .md-toc-inner { margin-left: 4em; }
.md-toc-h4 .md-toc-inner { margin-left: 6em; }
.md-toc-h5 .md-toc-inner { margin-left: 8em; }
.md-toc-h6 .md-toc-inner { margin-left: 10em; }
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  .md-toc-h3 .md-toc-inner { margin-left: 3.5em; }
  .md-toc-h4 .md-toc-inner { margin-left: 5em; }
  .md-toc-h5 .md-toc-inner { margin-left: 6.5em; }
  .md-toc-h6 .md-toc-inner { margin-left: 8em; }
}
a.md-toc-inner { font-size: inherit; font-style: inherit; font-weight: inherit; line-height: inherit; }
.footnote-line a:not(.reversefootnote) { color: inherit; }
.md-attr { display: none; }
.md-fn-count::after { content: "."; }
code, pre, samp, tt { font-family: var(--monospace); }
kbd { margin: 0px 0.1em; padding: 0.1em 0.6em; font-size: 0.8em; color: rgb(36, 39, 41); background: rgb(255, 255, 255); border: 1px solid rgb(173, 179, 185); border-radius: 3px; box-shadow: rgba(12, 13, 14, 0.2) 0px 1px 0px, rgb(255, 255, 255) 0px 0px 0px 2px inset; white-space: nowrap; vertical-align: middle; }
.md-comment { color: rgb(162, 127, 3); opacity: 0.8; font-family: var(--monospace); }
code { text-align: left; vertical-align: initial; }
a.md-print-anchor { white-space: pre !important; border-width: initial !important; border-style: none !important; border-color: initial !important; display: inline-block !important; position: absolute !important; width: 1px !important; right: 0px !important; outline: 0px !important; background: 0px 0px !important; text-decoration: initial !important; text-shadow: initial !important; }
.os-windows.monocolor-emoji .md-emoji { font-family: "Segoe UI Symbol", sans-serif; }
.md-diagram-panel > svg { max-width: 100%; }
[lang="flow"] svg, [lang="mermaid"] svg { max-width: 100%; height: auto; }
[lang="mermaid"] .node text { font-size: 1rem; }
table tr th { border-bottom: 0px; }
video { max-width: 100%; display: block; margin: 0px auto; }
iframe { max-width: 100%; width: 100%; border: none; }
.highlight td, .highlight tr { border: 0px; }
mark { background: rgb(255, 255, 0); color: rgb(0, 0, 0); }
.md-html-inline .md-plain, .md-html-inline strong, mark .md-inline-math, mark strong { color: inherit; }
.md-expand mark .md-meta { opacity: 0.3 !important; }
mark .md-meta { color: rgb(0, 0, 0); }
@media print {
  .typora-export h1, .typora-export h2, .typora-export h3, .typora-export h4, .typora-export h5, .typora-export h6 { break-inside: avoid; }
}
.md-diagram-panel .messageText { stroke: none !important; }
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.md-diagram-panel .edgeLabel rect { opacity: 1 !important; }
.md-require-zoom-fix foreignobject { font-size: var(--mermaid-font-zoom); }
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  .typora-export-sidebar, .typora-export-sidebar .outline-content { width: 240px; }
}
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}
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.outline-content ul { margin-top: 0px; margin-bottom: 0px; }
.outline-content strong { font-weight: 400; }
.outline-expander { width: 1rem; height: 1.42857rem; position: relative; display: table-cell; vertical-align: middle; cursor: pointer; padding-left: 4px; }
.outline-expander::before { content: ""; position: relative; font-family: Ionicons; display: inline-block; font-size: 8px; vertical-align: middle; }
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.outline-h4 > .outline-item { padding-left: 3em; }
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.outline-h6 > .outline-item { padding-left: 5em; }
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.outline-item-single .outline-expander::before, .outline-item-single .outline-expander:hover::before { display: none; }
.outline-item-open > .outline-item > .outline-expander::before { content: ""; }
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.md-inline-math-container mjx-container { zoom: 0.95; }


.CodeMirror { height: auto; }
.CodeMirror.cm-s-inner { background: inherit; }
.CodeMirror-scroll { overflow: auto hidden; z-index: 3; }
.CodeMirror-gutter-filler, .CodeMirror-scrollbar-filler { background-color: rgb(255, 255, 255); }
.CodeMirror-gutters { border-right: 1px solid rgb(221, 221, 221); background: inherit; white-space: nowrap; }
.CodeMirror-linenumber { padding: 0px 3px 0px 5px; text-align: right; color: rgb(153, 153, 153); }
.cm-s-inner .cm-keyword { color: rgb(119, 0, 136); }
.cm-s-inner .cm-atom, .cm-s-inner.cm-atom { color: rgb(34, 17, 153); }
.cm-s-inner .cm-number { color: rgb(17, 102, 68); }
.cm-s-inner .cm-def { color: rgb(0, 0, 255); }
.cm-s-inner .cm-variable { color: rgb(0, 0, 0); }
.cm-s-inner .cm-variable-2 { color: rgb(0, 85, 170); }
.cm-s-inner .cm-variable-3 { color: rgb(0, 136, 85); }
.cm-s-inner .cm-string { color: rgb(170, 17, 17); }
.cm-s-inner .cm-property { color: rgb(0, 0, 0); }
.cm-s-inner .cm-operator { color: rgb(152, 26, 26); }
.cm-s-inner .cm-comment, .cm-s-inner.cm-comment { color: rgb(170, 85, 0); }
.cm-s-inner .cm-string-2 { color: rgb(255, 85, 0); }
.cm-s-inner .cm-meta { color: rgb(85, 85, 85); }
.cm-s-inner .cm-qualifier { color: rgb(85, 85, 85); }
.cm-s-inner .cm-builtin { color: rgb(51, 0, 170); }
.cm-s-inner .cm-bracket { color: rgb(153, 153, 119); }
.cm-s-inner .cm-tag { color: rgb(17, 119, 0); }
.cm-s-inner .cm-attribute { color: rgb(0, 0, 204); }
.cm-s-inner .cm-header, .cm-s-inner.cm-header { color: rgb(0, 0, 255); }
.cm-s-inner .cm-quote, .cm-s-inner.cm-quote { color: rgb(0, 153, 0); }
.cm-s-inner .cm-hr, .cm-s-inner.cm-hr { color: rgb(153, 153, 153); }
.cm-s-inner .cm-link, .cm-s-inner.cm-link { color: rgb(0, 0, 204); }
.cm-negative { color: rgb(221, 68, 68); }
.cm-positive { color: rgb(34, 153, 34); }
.cm-header, .cm-strong { font-weight: 700; }
.cm-del { text-decoration: line-through; }
.cm-em { font-style: italic; }
.cm-link { text-decoration: underline; }
.cm-error { color: red; }
.cm-invalidchar { color: red; }
.cm-constant { color: rgb(38, 139, 210); }
.cm-defined { color: rgb(181, 137, 0); }
div.CodeMirror span.CodeMirror-matchingbracket { color: rgb(0, 255, 0); }
div.CodeMirror span.CodeMirror-nonmatchingbracket { color: rgb(255, 34, 34); }
.cm-s-inner .CodeMirror-activeline-background { background: inherit; }
.CodeMirror { position: relative; overflow: hidden; }
.CodeMirror-scroll { height: 100%; outline: 0px; position: relative; box-sizing: content-box; background: inherit; }
.CodeMirror-sizer { position: relative; }
.CodeMirror-gutter-filler, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-vscrollbar { position: absolute; z-index: 6; display: none; outline: 0px; }
.CodeMirror-vscrollbar { right: 0px; top: 0px; overflow: hidden; }
.CodeMirror-hscrollbar { bottom: 0px; left: 0px; overflow: auto hidden; }
.CodeMirror-scrollbar-filler { right: 0px; bottom: 0px; }
.CodeMirror-gutter-filler { left: 0px; bottom: 0px; }
.CodeMirror-gutters { position: absolute; left: 0px; top: 0px; padding-bottom: 10px; z-index: 3; overflow-y: hidden; }
.CodeMirror-gutter { white-space: normal; height: 100%; box-sizing: content-box; padding-bottom: 30px; margin-bottom: -32px; display: inline-block; }
.CodeMirror-gutter-wrapper { position: absolute; z-index: 4; background: 0px 0px !important; border: none !important; }
.CodeMirror-gutter-background { position: absolute; top: 0px; bottom: 0px; z-index: 4; }
.CodeMirror-gutter-elt { position: absolute; cursor: default; z-index: 4; }
.CodeMirror-lines { cursor: text; }
.CodeMirror pre { border-radius: 0px; border-width: 0px; background: 0px 0px; font-family: inherit; font-size: inherit; margin: 0px; white-space: pre; overflow-wrap: normal; color: inherit; z-index: 2; position: relative; overflow: visible; }
.CodeMirror-wrap pre { overflow-wrap: break-word; white-space: pre-wrap; word-break: normal; }
.CodeMirror-code pre { border-right: 30px solid transparent; width: fit-content; }
.CodeMirror-wrap .CodeMirror-code pre { border-right: none; width: auto; }
.CodeMirror-linebackground { position: absolute; inset: 0px; z-index: 0; }
.CodeMirror-linewidget { position: relative; z-index: 2; overflow: auto; }
.CodeMirror-wrap .CodeMirror-scroll { overflow-x: hidden; }
.CodeMirror-measure { position: absolute; width: 100%; height: 0px; overflow: hidden; visibility: hidden; }
.CodeMirror-measure pre { position: static; }
.CodeMirror div.CodeMirror-cursor { position: absolute; visibility: hidden; border-right: none; width: 0px; }
.CodeMirror div.CodeMirror-cursor { visibility: hidden; }
.CodeMirror-focused div.CodeMirror-cursor { visibility: inherit; }
.cm-searching { background: rgba(255, 255, 0, 0.4); }
span.cm-underlined { text-decoration: underline; }
span.cm-strikethrough { text-decoration: line-through; }
.cm-tw-syntaxerror { color: rgb(255, 255, 255); background-color: rgb(153, 0, 0); }
.cm-tw-deleted { text-decoration: line-through; }
.cm-tw-header5 { font-weight: 700; }
.cm-tw-listitem:first-child { padding-left: 10px; }
.cm-tw-box { border-style: solid; border-right-width: 1px; border-bottom-width: 1px; border-left-width: 1px; border-color: inherit; border-top-width: 0px !important; }
.cm-tw-underline { text-decoration: underline; }
@media print {
  .CodeMirror div.CodeMirror-cursor { visibility: hidden; }
}


/* meyer reset -- http://meyerweb.com/eric/tools/css/reset/ , v2.0 | 20110126 | License: none (public domain) */

@include-when-export url(https://fonts.loli.net/css?family=PT+Serif:400,400italic,700,700italic&subset=latin,cyrillic-ext,cyrillic,latin-ext);

/* =========== */

/* pt-serif-regular - latin */
/* pt-serif-italic - latin */
/* pt-serif-700 - latin */
/* pt-serif-700italic - latin */
:root {
	--active-file-bg-color: #dadada;
	--active-file-bg-color: rgba(32, 43, 51, 0.63);
	--active-file-text-color: white;
	--bg-color: #f3f2ee;
	--text-color: #1f0909;
	--control-text-color: #444;
	--rawblock-edit-panel-bd: #e5e5e5;

	--select-text-bg-color: rgba(32, 43, 51, 0.63);
  --select-text-font-color: white;
}

pre {
	--select-text-bg-color: #36284e;
	--select-text-font-color: #fff;
}

html {
	font-size: 16px;
	-webkit-font-smoothing: antialiased;
}

html, body {
	background-color: #f3f2ee;
	font-family: "PT Serif", 'Times New Roman', Times, serif;
	color: #1f0909;
	line-height: 1.5em;
}

/*#write {
	overflow-x: auto;
    max-width: initial;
	padding-left: calc(50% - 17em);
    padding-right: calc(50% - 17em);
}

@media (max-width: 36em) {
 	#write {
 		padding-left: 1em;
    	padding-right: 1em;
 	}
}*/

#write {
	max-width: 40em;
}

@media only screen and (min-width: 1400px) {
	#write {
			max-width: 914px;
	}
}

ol li {
	list-style-type: decimal;
	list-style-position: outside;
}
ul li {
	list-style-type: disc;
	list-style-position: outside;
}

ol,
ul {
	list-style: none;
}

blockquote,
q {
	quotes: none;
}
blockquote:before,
blockquote:after,
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</style><title>machinelearning</title>
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<body class='typora-export os-windows'><div class='typora-export-content'>
<div id='write'  class=''><h1 id='machine-learning机器学习'><span>Machine Learning(机器学习)</span></h1><h2 id='课程目的'><span>课程目的</span></h2><ul><li><span>对于机器学习算法模型，要能够知道它的应用场景和设计思路，要学会对解决相同问题的不同算法进行性能对比，从根本上思考它们的异同，并且了解不同模型的限制条件（linear，non-linear，worst case complexity，NP hard）。</span></li><li><span>给定某个具体的问题，要能够较快的识别出问题的类型（分类，回归，参数估计，标注，聚类，矩阵分解等），并且使用适当的模型来解决。</span></li></ul><h3 id='预备知识'><strong><span>预备知识</span></strong></h3><ul><li><span>线性代数，可以同时学习.</span></li><li><span>概率论与数理统计，可以同时学习.</span></li><li><span>Python，简明教程：</span><a href='https://www.liaoxuefeng.com/wiki/1016959663602400'><span>Python教程</span></a><span>.</span></li></ul><h2 id='参考教材'><span>参考教材</span></h2><p><span>主教材：</span><strong><a href='https://item.jd.com/12522197.html'><span>《统计学习方法》，第二版，李航</span></a></strong><span>，简称[LH]</span></p><p><span>参考教材：</span></p><ul><li><a href='https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf'><span>Bishop, </span><em><span>Pattern Recognition and Machine Learning</span></em></a><span>，简称[PRML];</span></li><li><a href='https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12_toc.pdf'><span>Hastie et al. </span><em><span>The Elements of Statistical Learning: Data Mining, Inference, and Prediction</span></em></a><span>，简称[ESL];</span></li><li><a href='https://item.jd.com/12762673.html'><span>《机器学习》，周志华</span></a><span>，简称[Zhou];</span></li><li><a href='https://datawhalechina.github.io/pumpkin-book/#/'><span>《机器学习公式详解》</span></a><span>，南瓜书，配套[Zhou];</span></li><li><a href='https://static1.squarespace.com/static/5ff2adbe3fe4fe33db902812/t/6062a083acbfe82c7195b27d/1617076404560/ISLR%2BSeventh%2BPrinting.pdf'><span>James et al. </span><em><span>An Introduction to Statistical Learning with Applications in R</span></em></a><span>，简称[ISLR];</span></li><li><a href='http://incompleteideas.net/book/RLbook2020.pdf'><span>Sutton et al. </span><em><span>Reinforcement Learning: An Introduction</span></em></a><span>，简称[Sutton];</span></li><li><a href='https://zh.d2l.ai/'><span>Zhang et al. </span><em><span>Dive into Deep Learning</span></em></a><span>，简称[D2L].</span></li></ul><blockquote><p><span>以上教材均给出了免费电子版链接，直接点击上述链接即可，没有免费电子版的给出了购买链接，请支持正版。</span></p></blockquote><h2 id='参考课程'><span>参考课程</span></h2><p><span>除了参考教材以外，还可以参考以下的视频课程：</span></p><ul><li><a href='https://www.bilibili.com/video/BV1fv411r7z1?from=search&amp;seid=12434442908364475673'><span>机器学习与量化投资</span></a><span>，复旦大学，孙健，配套[LH];</span></li><li><a href='https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/'><span>Statistical Learning</span></a><span>，Stanford，配套[ISLR]，</span><a href='https://www.bilibili.com/video/BV11t411A7Ym?from=search&amp;seid=1009085535004050656'><span>B站源</span></a><span>;</span></li><li><a href='https://www.bilibili.com/video/BV14p4y1h7ay'><span>机器学习实践部分</span></a><span>，信阳师范学院，配套[Zhou, LH];</span></li><li><a href='https://www.bilibili.com/video/BV1KA41157yn?from=search&amp;seid=8529535487059534744'><span>EECS 189 Machine Learning</span></a><span>，UC berkeley，</span><a href='https://people.eecs.berkeley.edu/~jrs/189/'><span>课程主页</span></a><span>;</span></li><li><a href='https://www.bilibili.com/video/BV16J411t71N?from=search&amp;seid=12250428307825824060'><span>CS 229 Machine Learning</span></a><span>，Stanford University，</span><a href='http://cs229.stanford.edu/'><span>课程主页</span></a><span>;</span></li><li><a href='https://www.bilibili.com/video/BV1aE411o7qd?from=search&amp;seid=3059037172455438333'><span>机器学习白板推导</span></a><span>，B站UP主：shuhuai008;</span></li><li><a href='https://courses.d2l.ai/zh-v2/'><span>动手学深度学习在线课程</span></a><span>，配套[D2L].</span></li></ul><h3 id='学习形式'><strong><span>学习形式</span></strong></h3><ul><li><p><span>我们主要通过内容专题+论文研习+课程项目的形式来学习。</span></p></li><li><p><span>同学们根据课程安排中的 </span><span style="color:red"><span>READINGS</span></span><span> 部分来学习各个专题内容，专题主要包括：</span></p><blockquote><ul><li><span>传统机器学习，主要参考[LH]</span></li><li><span>强化学习基础，主要参考[Sutton]</span></li><li><span>深度学习基础，主要参考[D2L]</span></li></ul></blockquote><p><span style="color:green"><span>并不一定要阅读完[READINGS]部分的所有材料，保证能够理解对应的专题内容，能够按时完成课程作业即可。</span></span></p><p><span>课程项目包括平时项目和期末项目：</span></p><blockquote><ul><li><span>平时项目，天池练习赛，每两周一次，需要提交项目报告;</span></li><li><span>期末项目，通过选题来完成项目，选题范围覆盖整个整个课程内容.</span></li></ul></blockquote></li><li><p><span>除了基本的课程内容专题学习，每周还会有论文研习部分（1-2篇/周），相关论文的主题为课程基本内容的扩展，每周末将会组织4位学员进行30分钟的论文综述汇报，报告人顺序待定，报告具体时间段，结合学员具体情况来定。</span></p></li><li><p><span>阅读学术论文方法论的参考资料：</span></p><ul><li><a href='https://mp.weixin.qq.com/s/83V--4IdtOJ4pP-muEiUVw'><span>Andrew Ng(吴恩达)关于机器学习职业生涯以及阅读论文的一些建议</span></a></li><li><a href='https://bicmr.pku.edu.cn/~wenzw/paper/read-paper-wenzw.pdf'><span>学术论文阅读与写作</span></a></li></ul></li></ul><h5 id='课程作业'><span>课程作业：</span></h5><ul><li><p><span>课程作业组成：</span></p><blockquote><p><span>3 projects + 1 final projects</span></p><p><span>3 paper reports + 6 online meetings</span></p></blockquote><p><span>最终成绩为各组成部分的得分综合。</span></p></li><li><p><span>项目完成之后需要各位交上不含数据的源代码以及实验报告，课程负责人将进行代码检查，并且根据完成情况给分，实验报告分为三个部分：</span></p><ol start='' ><li><strong><span>问题简述</span></strong></li><li><strong><span>解决思路</span></strong></li><li><strong><span>实验结果</span></strong></li></ol></li><li><p><span>除了课程项目，还需要</span><strong><span>每位参与课程的成员</span></strong><span>提交当周的论文阅读报告，可以以PPT或者Beamer的形式进行总结，可以从这几方面进行总结：</span></p><ul><li><span>该论文是为了解决什么问题；</span></li><li><span>为了解决这个问题，前人采取了哪些手段，有哪些缺点；</span></li><li><span>本论文提出了什么手段来解决这个问题；</span></li><li><span>找得到相关的开源实现吗？找得到就跑一跑，看看效果如何；</span></li><li><span>如果是网络结构类，请简要描述网络的结构层次，如果是算法类，请描述是用什么办法来求解这个算法的；</span></li><li><span>深入思考论文有哪些地方是有问题并且待改进的，分条陈述。</span></li></ul></li></ul><h5 id='课程安排'><span>课程安排：</span></h5><figure><table><thead><tr><th style='text-align:center;' ><span>WEEK</span></th><th><span>Date</span></th><th style='text-align:left;' ><span>Topic</span></th><th style='text-align:left;' ><span>ReadINGS</span></th><th><span>PAPERS</span></th><th style='text-align:left;' ><span>projects</span></th></tr></thead><tbody><tr><td style='text-align:center;' ><span>1</span></td><td><span>Mon.7/12</span></td><td style='text-align:left;' ><span>Machine Learning Basics</span></td><td style='text-align:left;' ><span>[Zhou, Chapter 1, 2], [LH, Chapter1]</span></td><td><a href='http://pages.stat.wisc.edu/~loh/treeprogs/guide/wires11.pdf'><span>Loh (2011)</span></a><span>, </span><a href='https://www.ling.upenn.edu/courses/Fall_2007/cogs501/Gallant1990.pdf'><span>Gallant (1990)</span></a></td><td style='text-align:left;' ><a href='https://tianchi.aliyun.com/competition/entrance/231784/introduction'><span>Project 1</span></a><span>，备注：可以参考比赛页面的【</span><strong><span>赛题与数据</span></strong><span>】一栏的【</span><strong><span>四、Datawhale赛事专题学习资料</span></strong><span>】，手把手教学</span></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Tue.7/13</span></td><td style='text-align:left;' ><span>Perceptron</span></td><td style='text-align:left;' ><span>[LH, Chapter 2]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Wed.7/14</span></td><td style='text-align:left;' ><span>Perceptron</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Thu.7/15</span></td><td style='text-align:left;' ><span>Perceptron</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Fri.7/16</span></td><td style='text-align:left;' ><span>K-NN</span></td><td style='text-align:left;' ><span>[LH, Chapter 3]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sat.7/17</span></td><td style='text-align:left;' ><span>K-NN</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' ><span>Online Meeting</span></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sun.7/18</span></td><td style='text-align:left;' ><span>Decision Tree</span></td><td style='text-align:left;' ><span>[LH, Chapter 5]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' ><span>2</span></td><td><span>Mon.7/19</span></td><td style='text-align:left;' ><span>Descision Tree</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Tue.7/20</span></td><td style='text-align:left;' ><span>Hierarchical clustering</span></td><td style='text-align:left;' ><span>[LH, Chapter 14]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Wed.7/21</span></td><td style='text-align:left;' ><span>K-means</span></td><td style='text-align:left;' ><span>[LH, Chapter 14]</span></td><td><a href='http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf'><span>Arthur et al. (2007)</span></a></td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Thu.7/22</span></td><td style='text-align:left;' ><span>K-means</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Fri.7/23</span></td><td style='text-align:left;' ><span>K-means</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sat.7/24</span></td><td style='text-align:left;' ><span>Naive Bayes</span></td><td style='text-align:left;' ><span>[LH, Chapter 4], [Zhou, Chapter 7], </span><a href='https://zhuanlan.zhihu.com/p/46737512'><span>MLE, MAP</span></a><span>, </span><a href='http://cs229.stanford.edu/notes2020spring/cs229-notes2.pdf'><span>CS 229 note 2</span></a></td><td>&nbsp;</td><td style='text-align:left;' ><span>Online Meeting</span></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sun.7/25</span></td><td style='text-align:left;' ><span>Naive Bayes</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' ><span style="color:red"><span>Project 1, Report1  deadline</span></span></td></tr><tr><td style='text-align:center;' ><span>3</span></td><td><span>Mon.7/26</span></td><td style='text-align:left;' ><span>Linear Regression</span></td><td style='text-align:left;' ><span>[Zhou, Chapter 3.1], </span><a href='https://www.eecs189.org/static/notes/n2.pdf'><span>EECS 189 notes 2</span></a><span>，[ISLR Chapter 3]</span></td><td><a href='https://proceedings.neurips.cc/paper/2001/file/2eace51d8f796d04991c831a07059758-Paper.pdf'><span>Zhu et al. (2001)</span></a><span>，</span><a href='http://www.svms.org/regression/SmSc98.pdf'><span>Smola et al. (1998)</span></a></td><td style='text-align:left;' ><a href='https://www.heywhale.com/home/competition/609cc718ca31cd0017835fdc'><span>Project 2</span></a><span>，备注：此项目</span><strong><span>相对较难</span></strong><span>，可以尝试组队完成，每队不超过3人，组队后请将名单报至负责人。</span></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Tue.7/27</span></td><td style='text-align:left;' ><span>Kernels and Ridge Regression</span></td><td style='text-align:left;' ><a href='https://www.eecs189.org/static/notes/n8.pdf'><span>EECS 189 notes 8</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Wed.7/28</span></td><td style='text-align:left;' ><span>Kernels and Ridge Regression</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Thu.7/29</span></td><td style='text-align:left;' ><span>Regularization and Model Selection</span></td><td style='text-align:left;' ><span>[Zhou, Chapter 11]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Fri.7/30</span></td><td style='text-align:left;' ><span>Regularization and Model Selection</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sat.7/31</span></td><td style='text-align:left;' ><span>Logistic Regression</span></td><td style='text-align:left;' ><span>[LH, Chapter 6]</span></td><td>&nbsp;</td><td style='text-align:left;' ><span>Online Meeting</span></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sun.8/1</span></td><td style='text-align:left;' ><span>SVM</span></td><td style='text-align:left;' ><span>[LH, Chapter 7]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' ><span>4</span></td><td><span>Mon.8/2</span></td><td style='text-align:left;' ><span>SVM</span></td><td style='text-align:left;' ><span>[LH, Chapter 7]</span></td><td><a href='http://statweb.stanford.edu/~jhf/ftp/trebst.pdf'><span>Friedman (1999)</span></a><span>，</span><a href='https://arxiv.org/pdf/1603.02754.pdf'><span>Chen et al. (2016)</span></a></td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Tue.8/3</span></td><td style='text-align:left;' ><span>Boosting, Adaboost</span></td><td style='text-align:left;' ><span>[LH, Chapter 8], </span><a href='http://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf'><span>An introduction to boosting</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Wed.8/4</span></td><td style='text-align:left;' ><span>Boosting, Adaboost</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Thu.8/5</span></td><td style='text-align:left;' ><span>Bagging</span></td><td style='text-align:left;' ><span>[Zhou, Chapter 8.3], </span><a href='https://statistics.berkeley.edu/sites/default/files/tech-reports/421.pdf'><span>Baggin Predictors</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Fri.8/6</span></td><td style='text-align:left;' ><span>Random Forest</span></td><td style='text-align:left;' ><span>[Zhou, Chapter 8.3], </span><a href='https://link.springer.com/content/pdf/10.1023/A:1010933404324.pdf'><span>Random Forest</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sat.8/7</span></td><td style='text-align:left;' ><span>Random Forest</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' ><span>Online Meeting</span></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sun.8/8</span></td><td style='text-align:left;' ><span>HMM</span></td><td style='text-align:left;' ><span>[LH, Chapter 10]</span></td><td>&nbsp;</td><td style='text-align:left;' ><span style="color:red"><span>Project 2, Report 2 deadline</span></span></td></tr><tr><td style='text-align:center;' ><span>5</span></td><td><span>Mon.8/9</span></td><td style='text-align:left;' ><span>HMM</span></td><td style='text-align:left;' >&nbsp;</td><td><a href='https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf'><span>Koren (2009)</span></a></td><td style='text-align:left;' ><a href=''><span>Project 3</span></a></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Tue.8/10</span></td><td style='text-align:left;' ><span>CRF</span></td><td style='text-align:left;' ><span>[LH, Chapter 11]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Wed.8/11</span></td><td style='text-align:left;' ><span>EM Algorithms</span></td><td style='text-align:left;' ><span>[LH, Chapter 9], [Zhou, Chapter 7.6]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Thu.8/12</span></td><td style='text-align:left;' ><span>GMM</span></td><td style='text-align:left;' ><a href='http://cs229.stanford.edu/notes2020spring/cs229-notes7b.pdf'><span>CS 229 notes 7b</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Fri.8/13</span></td><td style='text-align:left;' ><span>SVD, PCA</span></td><td style='text-align:left;' ><span>[LH, Chapter 15, 16]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sat.8/14</span></td><td style='text-align:left;' ><span>SVD, PCA</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' ><span>Online Meeting</span></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sun.8/15</span></td><td style='text-align:left;' ><span>LDA</span></td><td style='text-align:left;' ><span>[Zhou, Chapter 3.4]</span></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' ><span>6</span></td><td><span>Mon.8/16</span></td><td style='text-align:left;' ><span>Optimization, Gradient Descent</span></td><td style='text-align:left;' ><span>[LH, 附录A], </span><a href='https://www.eecs189.org/static/notes/n12.pdf'><span>EECS 189 note 12</span></a></td><td><a href='http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf'><span>Lecun et al. (1998)</span></a><span>，</span><a href='https://arxiv.org/pdf/1412.3555.pdf'><span>Chung et al. (2014)</span></a></td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Tue.8/17</span></td><td style='text-align:left;' ><span>MLP</span></td><td style='text-align:left;' ><span>[D2L, Chapter 3]，[Zhou, Chapter 5]，</span><a href='https://www.deeplearningbook.org/contents/mlp.html'><span>Deep Learning, Chapter 6</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Wed.8/18</span></td><td style='text-align:left;' ><span>MLP</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Thu.8/19</span></td><td style='text-align:left;' ><span>CNN</span></td><td style='text-align:left;' ><span>[D2L, Chapter 5], </span><a href='https://www.deeplearningbook.org/contents/convnets.html'><span>Deep Learning, Chapter 9</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Fri.8/20</span></td><td style='text-align:left;' ><span>CNN</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sat.8/21</span></td><td style='text-align:left;' ><span>RNN</span></td><td style='text-align:left;' ><span>[D2L, Chapter 6], </span><a href='https://www.deeplearningbook.org/contents/rnn.html'><span>Deep Learning, Chapter 10</span></a></td><td>&nbsp;</td><td style='text-align:left;' ><span>Online Meeting</span></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sun.8/22</span></td><td style='text-align:left;' ><span>RNN</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' ><span style="color:red"><span>Project 3, Report 3 deadline</span></span></td></tr><tr><td style='text-align:center;' ><span>7</span></td><td><span>Mon.8/23</span></td><td style='text-align:left;' ><span>Markov Decision Process</span></td><td style='text-align:left;' ><span>[Sutton, Chapter 3], </span><a href='http://cs229.stanford.edu/notes2020fall/notes2020fall/cs229-notes12.pdf'><span>CS 229 note 12</span></a><span>,</span></td><td>&nbsp;</td><td style='text-align:left;' ><a href=''><span>Final Project</span></a></td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Tue.8/24</span></td><td style='text-align:left;' ><span>MDP</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Wed.8/25</span></td><td style='text-align:left;' ><span>MDP</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Thu.8/26</span></td><td style='text-align:left;' ><span>TD-Learnig and Q-learning</span></td><td style='text-align:left;' ><a href='https://ieor8100.github.io/rl/docs/Lecture%202-%20Q%20learning%20tabular.pdf'><span>IEOR 8100 Lecture Notes 2</span></a><span>，</span><a href='https://bicmr.pku.edu.cn/~wenzw/bigdata/lect-DQN.pdf'><span>Peking U Lecture Notes DQN</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Fri.8/27</span></td><td style='text-align:left;' ><span>TD-Learnig and Q-learning</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sat.8/28</span></td><td style='text-align:left;' ><span>Policy Gradient Methods</span></td><td style='text-align:left;' ><span>[Sutton, Chapter 13], </span><a href='https://bicmr.pku.edu.cn/~wenzw/bigdata/lect-policy.pdf'><span>Peking U Lecture Notes Policy Gradient Methods</span></a></td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Sun.8/29</span></td><td style='text-align:left;' ><span>Policy Gradient Methods</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' >&nbsp;</td></tr><tr><td style='text-align:center;' >&nbsp;</td><td><span>Mon.8/30</span></td><td style='text-align:left;' ><span>Policy Gradient Methods</span></td><td style='text-align:left;' >&nbsp;</td><td>&nbsp;</td><td style='text-align:left;' ><span style="color:red"><span>Final Project deadline</span></span></td></tr></tbody></table></figure><h5 id='作业说明'><span>作业说明</span></h5><ol start='' ><li><p><strong><u><span>作业缴交形式</span></u></strong><span>：报告</span><strong><span>需要交代码文件+报告文档，文档采用</span><mjx-container class="MathJax" jax="SVG" style="position: relative;"><svg xmlns="http://www.w3.org/2000/svg" width="6.293ex" height="2.108ex" role="img" focusable="false" viewBox="0 -716.3 2781.3 931.8" 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display="inline"><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>L</mi><mspace width="-.325em"></mspace><mpadded height="+.21em" depth="-.21em" voffset="+.21em"><mrow data-mjx-texclass="ORD"><mstyle displaystyle="false" scriptlevel="1"><mrow data-mjx-texclass="ORD"><mi>A</mi></mrow></mstyle></mrow></mpadded><mspace width="-.17em"></mspace><mi>T</mi><mspace width="-.14em"></mspace><mpadded height="-.5ex" depth="+.5ex" voffset="-.5ex"><mrow data-mjx-texclass="ORD"><mi>E</mi></mrow></mpadded><mspace width="-.115em"></mspace><mi>X</mi></math></mjx-assistive-mml></mjx-container><script type="math/tex">\LaTeX</script><span>排版，代码独立出来，不要作为报告文档的附录</span></strong><span>，邮件发送到到：</span><a href='mailto:scaudatahub_ds_center@outlook.com' target='_blank' class='url'>scaudatahub_ds_center@outlook.com</a><span>，邮件主题格式同下面的作业命名格式，缴交日期是在课程安排里面的</span><strong><span>正常缴交截止日期的中午十二点</span></strong><span>之前，</span><strong><span>论文报告</span></strong><span>(PPT或者Beamer)缴交截至日期相同，并与项目代码zip和报告文件pdf一并打包为一个zip文件按【3】中的命名格式命名。</span></p></li><li><p><strong><u><span>邮件主题命名格式</span></u></strong><span>：采用英文命名，具体格式为：</span><strong><span>SCAUDataHub</span><span>_</span><span>名字</span><span>_</span><span>科目</span><span>_</span><span>课程时间</span><span>_</span><span>hw+作业第几次</span></strong><span>，例如：张三机器学习2021年夏季的第一次作业，其对应的主题格式即为</span></p><pre class="md-fences md-end-block ty-contain-cm modeLoaded" spellcheck="false" lang=""><div class="CodeMirror cm-s-inner cm-s-null-scroll CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 10px; left: 4px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">SCAUDataHub_ZhangSan_MachineLearning_21SU_hw1</span></pre></div></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 24px;"></div><div class="CodeMirror-gutters" style="display: none; height: 24px;"></div></div></div></pre><blockquote><p><span>若出现重名到时候再处理。</span></p></blockquote></li><li><p><strong><u><span>作业命名格式</span></u></strong><span>：采用英文命名，具体格式为：</span><strong><span>SCAUDataHub_名字</span><span>_</span><span>科目</span><span>_</span><span>hw+作业第几次</span></strong><span>，例如：张三机器学习2021年夏季的第一次作业，其对应的作业附件格式即为：</span></p><pre class="md-fences md-end-block ty-contain-cm modeLoaded" spellcheck="false" lang=""><div class="CodeMirror cm-s-inner cm-s-null-scroll CodeMirror-wrap" lang=""><div style="overflow: hidden; position: relative; width: 3px; height: 0px; top: 10px; left: 4px;"><textarea autocorrect="off" autocapitalize="off" spellcheck="false" tabindex="0" style="position: absolute; bottom: -1em; padding: 0px; width: 1000px; height: 1em; outline: none;"></textarea></div><div class="CodeMirror-scrollbar-filler" cm-not-content="true"></div><div class="CodeMirror-gutter-filler" cm-not-content="true"></div><div class="CodeMirror-scroll" tabindex="-1"><div class="CodeMirror-sizer" style="margin-left: 0px; margin-bottom: 0px; border-right-width: 0px; padding-right: 0px; padding-bottom: 0px;"><div style="position: relative; top: 0px;"><div class="CodeMirror-lines" role="presentation"><div role="presentation" style="position: relative; outline: none;"><div class="CodeMirror-measure"><pre><span>xxxxxxxxxx</span></pre></div><div class="CodeMirror-measure"></div><div style="position: relative; z-index: 1;"></div><div class="CodeMirror-code" role="presentation"><div class="CodeMirror-activeline" style="position: relative;"><div class="CodeMirror-activeline-background CodeMirror-linebackground"></div><div class="CodeMirror-gutter-background CodeMirror-activeline-gutter" style="left: 0px; width: 0px;"></div><pre class=" CodeMirror-line " role="presentation"><span role="presentation" style="padding-right: 0.1px;">SCAUDataHub_ZhangSan_MachineLearning_21SU_hw1.zip</span></pre></div></div></div></div></div></div><div style="position: absolute; height: 0px; width: 1px; border-bottom: 0px solid transparent; top: 24px;"></div><div class="CodeMirror-gutters" style="display: none; height: 24px;"></div></div></div></pre><blockquote><p><span>若出现重名到时候再处理.</span></p></blockquote></li><li><p><u><strong><span>考核形式</span></strong></u><span>：本次的考核内容即为所有的课程作业得分总和，最后通过综合表现进行评价排名，并以此为标准进行人员筛选工作。</span></p></li><li><p><strong><u><span>附加说明:</span></u></strong><span> 可以进行讨论，但是禁止代码抄袭，在截止日之后交作业，迟交 </span><mjx-container class="MathJax" jax="SVG" style="position: relative;"><svg xmlns="http://www.w3.org/2000/svg" width="1.179ex" height="1.595ex" role="img" focusable="false" viewBox="0 -694 521 705" xmlns:xlink="http://www.w3.org/1999/xlink" 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